Mass spectrometry‐based forest tree metabolomics

Abstract

Research in forest tree species has advanced slowly when compared with other agricultural crops and model organisms, mainly due to the long‐life cycles, large genome sizes, and lack of genomic tools. Additionally, trees are complex matrices, and the presence of interferents (e.g., oleoresins and cellulose) challenges the analysis of tree tissues with mass spectrometry (MS)‐based analytical platforms. In this review, advances in MS‐based forest tree metabolomics are discussed. Given their economic and ecological significance, particular focus is given to Pinus, Quercus, and Eucalyptus forest tree species to better understand their metabolite responses to abiotic and biotic stresses in the current climate change scenario. Furthermore, MS‐based metabolomics technologies produce large and complex datasets that require expertize to adequately manage, process, analyze, and store the data in dedicated repositories. To ensure that the full potential of forest tree metabolomics data are translated into new knowledge, these data should comply with the FAIR principles (i.e., Findable, Accessible, Interoperable, and Re‐usable). It is essential that adequate standards are implemented to annotate metadata from forest tree metabolomics studies as is already required by many science and governmental agencies and some major scientific publishers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:126–157, 2021.

Metabolomic tools used in marine natural product drug discovery

“…It enables a unique "top-down approach" for the study of complex biological systems [48]. The variety and wide range of analytical techniques that includes mass spectrometry [50][51][52][53][54][55][56], NMR spectroscopy [57][58][59][60][61][62][63], and infrared spectrophotometry [64][65][66][67][68][69], used within metabolomics has contributed to its rapid growth. The current tools made it possible to study thousands of metabolites in complex biological samples [70].…”

Experimental Design and Sample Preparation in Forest Tree Metabolomics

“…In forest tree research, GC coupled to either a time-of-flight MS (GC-TOF-MS) or a fast scanning quadrupole MS (GC-qMS) have often been employed for high-throughput plant primary metabolite profiling allowing the measurement of complex mixtures of primary metabolites (e.g., organic acids, sugars, sugar alcohols, amino acids) in a single extract [8]. GC-TOF-MS shows numerous advantages over GC-qMS, namely, higher mass accuracy, higher duty cycles, and faster acquisition rates that ultimately contribute to a better deconvolution of overlapping peaks and higher sample throughput [2,3,65].…”

“…Forest trees are species with long life cycles, and details of the experimental metadata (e.g., parental original or field growth conditions) are often not described. To re-use data derived from these studies, the description of the metadata should include detailed information of the harvested material (e.g., geographical location, growth conditions, biological growth stages, and phenological parameters) [8]. These parameters might reflect adaptive traits mediated by epigenetic changes that affect the material under study [109,110].…”

“…Forest tree metabolomics represents additional challenges when compared to other plant metabolomics studies. These include an experimental design that takes into account the long life cycle and the genetic variability of forest tree species as well the presence of interferents that can require additional steps during sample preparation (e.g., additional concentration steps) [8]. In this review, we highlight the major challenges when setting up an MS-based forest tree metabolomics experiment.…”

Metabolomic Variability in the Volatile Composition of Essential Oils from Pinus pinea and P. pinaster

“…The occurrence of EO chemotypes (i.e., qualitative and quantitative differences in the chemical composition between populations within the same species) is fairly common, namely in forest trees. EO chemotypes are another source of variation among species and have been strongly linked with geographical variation, being able to reflect the diverse environmental conditions to which pines are exposed [3,[10][11][12]. Chemical diversity in these species can lead to increased variability in research results and ultimately influence comparisons between different studies.…”